Financial distress prediction using SVM ensemble based on earnings manipulation and fuzzy integral

Abstract

Financial distress prediction (FDP) has received considerable attention from both practitioners and researchers. This paper proposes a novel support vector machine (SVM) classifier ensemble framework based on earnings manipulation and fuzzy integral for FDP named SEMFI. We use financial data from t he previous three years to predict companies’ current financial situation and divide the companies in each year into different categories according to whether they manipulate the earnings. Then, SVM is trained on different categories. The outputs of SVM are combined by fuzzy integral which adopts a new fuzzy measure determination method. This method considers the fact that recent financial data are more valuable for FDP. Additionally, when using the model trained by historical data for FDP, the external environment may have dramatically changed. Therefore, a fuzzy measure dynamic adjustment method is proposed by considering the confidence of each single classifier’s output, the consistency between each single classifier’s output and the diversity among classifiers. To verify the performance of SEMFI, an empirical study using real financial data is conducted. The results indicate that the introduction of earnings manipulation, the new fuzzy measure determination and dynamic adjustment method to FDP can significantly enhance the prediction performance.

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